StHGC: a self-supervised graph representation learning for spatial domain recognition with hybrid graph and spatial regularization.

ABSTRACT: Advancements in spatial transcriptomics (ST) technology have enabled the analysis of gene expression while preserving cellular spatial information, greatly enhancing our understanding of cellular interactions within tissues. Accurate identification of spatial domains is crucial for comprehending tissue organization. However, the effective integration of spatial location and gene expression still faces significant challenges. To address this challenge, we propose a novel self-supervised graph representation learning framework named stHGC for identifying spatial domains. Firstly, a hybrid neighbor graph is constructed by integrating different similarity metrics to represent spatial proximity and high-dimensional gene expression features. Secondly, a self-supervised graph representation learn

SUBMITTER: Wang R 

PROVIDER: S-EPMC11663487 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature